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| CMS-HIG-17-018 ; CERN-EP-2018-017 | ||
| Evidence for associated production of a Higgs boson with a top quark pair in final states with electrons, muons, and hadronically decaying $\tau$ leptons at $\sqrt{s} = $ 13 TeV | ||
| CMS Collaboration | ||
| 14 March 2018 | ||
| JHEP 08 (2018) 066 | ||
| Abstract: Results of a search for the standard model Higgs boson produced in association with a top quark pair ($\mathrm{ t \bar{t} H }$) in final states with electrons, muons, and hadronically decaying $\tau$ leptons are presented. The analyzed data set corresponds to an integrated luminosity of 35.9 fb$^{-1}$ recorded in proton-proton collisions at $\sqrt{s} = $ 13 TeV by the CMS experiment in 2016. The sensitivity of the search is improved by using matrix element and machine learning methods to separate the signal from backgrounds. The measured signal rate amounts to 1.23$^{+0.45}_{-0.43}$ times the production rate expected in the standard model, with an observed (expected) significance of 3.2$\sigma$ (2.8$\sigma$), which represents evidence for $\mathrm{ t \bar{t} H }$ production in those final states. An upper limit on the signal rate of 2.1 times the standard model production rate is set at 95% confidence level. | ||
| Links: e-print arXiv:1803.05485 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; HepData record ; CADI line (restricted) ; | ||
| Figures & Tables | Summary | Additional Figures | References | CMS Publications |
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| Figures | |
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Figure 1:
An example of a Feynman diagram for $ {\mathrm {t}} {\overline {\mathrm {t}}} {{\mathrm {H}}}$ production, with subsequent decay of the Higgs boson to a pair of $ {\tau}$ leptons. |
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Figure 2:
Distributions in the discriminating observables used for the signal extraction in the $1 {\ell}+2 {{\tau}_{\mathrm {h}}} $ category (top left) and in different subcategories of the $2 {\ell} {\text {ss}}$ category (top right and bottom row), compared to the SM expectation for the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal and for background processes. A BDT trained to separate the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal from the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\text {+jets}}$ background is used in the $1 {\ell}+2 {{\tau}_{\mathrm {h}}} $ category, while a $ {\mathrm {D}_{\text {MVA}}}$ variable, combining the outputs of two BDTs trained to discriminate the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal from the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\mathrm {V}}$ and $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\text {+jets}}$ backgrounds respectively, is used in the $2 {\ell} {\text {ss}}$ subcategories. The distributions expected for signal and background processes are shown for the values of nuisance parameters obtained from the combined ML fit and $\mu = \hat{\mu}=1.23$, corresponding to the best-fit value from the ML fit. |
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Figure 2-a:
Distribution in the discriminating observable used for the signal extraction in the $1 {\ell}+2 {{\tau}_{\mathrm {h}}} $ category, compared to the SM expectation for the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal and for background processes. A BDT trained to separate the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal from the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\text {+jets}}$ background is used in the $1 {\ell}+2 {{\tau}_{\mathrm {h}}} $ category, while a $ {\mathrm {D}_{\text {MVA}}}$ variable, combining the outputs of two BDTs trained to discriminate the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal from the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\mathrm {V}}$ and $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\text {+jets}}$ backgrounds respectively, is used in the $2 {\ell} {\text {ss}}$ subcategories. The distributions expected for signal and background processes are shown for the values of nuisance parameters obtained from the combined ML fit and $\mu = \hat{\mu}=1.23$, corresponding to the best-fit value from the ML fit. |
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Figure 2-b:
Distribution in the discriminating observable used for the signal extraction in the $\mathrm{ee} {\text {ss}}$ category, compared to the SM expectation for the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal and for background processes. A BDT trained to separate the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal from the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\text {+jets}}$ background is used in the $1 {\ell}+2 {{\tau}_{\mathrm {h}}} $ category, while a $ {\mathrm {D}_{\text {MVA}}}$ variable, combining the outputs of two BDTs trained to discriminate the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal from the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\mathrm {V}}$ and $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\text {+jets}}$ backgrounds respectively, is used in the $2 {\ell} {\text {ss}}$ subcategories. The distributions expected for signal and background processes are shown for the values of nuisance parameters obtained from the combined ML fit and $\mu = \hat{\mu}=1.23$, corresponding to the best-fit value from the ML fit. |
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Figure 2-c:
Distribution in the discriminating observable used for the signal extraction in the $\mathrm{e}\mu {\text {ss}}$ category, compared to the SM expectation for the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal and for background processes. A BDT trained to separate the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal from the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\text {+jets}}$ background is used in the $1 {\ell}+2 {{\tau}_{\mathrm {h}}} $ category, while a $ {\mathrm {D}_{\text {MVA}}}$ variable, combining the outputs of two BDTs trained to discriminate the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal from the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\mathrm {V}}$ and $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\text {+jets}}$ backgrounds respectively, is used in the $2 {\ell} {\text {ss}}$ subcategories. The distributions expected for signal and background processes are shown for the values of nuisance parameters obtained from the combined ML fit and $\mu = \hat{\mu}=1.23$, corresponding to the best-fit value from the ML fit. |
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Figure 2-d:
Distribution in the discriminating observable used for the signal extraction in the $ \mu\mu {\text {ss}}$ category, |
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Figure 3:
Distributions in the discriminating observables used for the signal extraction in the "no-missing-jet'' (top left) and "missing-jet'' (top right) subcategories of the $2 {\ell} {\text {ss}}+1 {{\tau}_{\mathrm {h}}} $ category, the $3 {\ell}$ category (bottom left), and the $3 {\ell}+ 1 {{\tau}_{\mathrm {h}}} $ category (bottom right), compared to the SM expectation for the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal and for background processes. The MEM discriminant $ {\text {LR}}(2 {\ell} {\text {ss}}+1 {{\tau}_{\mathrm {h}}})$ is used in the $2 {\ell} {\text {ss}}+1 {{\tau}_{\mathrm {h}}} $ subcategories, while a $ {\mathrm {D}_{\text {MVA}}}$ variable, combining the outputs of two BDTs trained to discriminate the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal from the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\mathrm {V}}$ and $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\text {+jets}}$ backgrounds respectively, is used in the $3 {\ell}$ and $3 {\ell}+ 1 {{\tau}_{\mathrm {h}}} $ categories. The distributions expected for signal and background processes are shown for the values of nuisance parameters obtained from the combined ML fit and $\mu = \hat{\mu} = $ 1.23, corresponding to the best-fit value from the ML fit. The lowest bin of the MEM discriminant in the "missing-jet'' subcategory of the $2 {\ell} {\text {ss}}+1 {{\tau}_{\mathrm {h}}} $ category collects events for which the kinematics of the reconstructed objects is not compatible with the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$, $ {{\mathrm {H}}}\to {\tau} {\tau}$ signal hypothesis. |
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Figure 3-a:
Distribution in the discriminating observable used for the signal extraction in the "no-missing-jet'' subcategory of the $2 {\ell} {\text {ss}}+1 {{\tau}_{\mathrm {h}}} $ category, compared to the SM expectation for the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal and for background processes. The MEM discriminant $ {\text {LR}}(2 {\ell} {\text {ss}}+1 {{\tau}_{\mathrm {h}}})$ is used. The distributions expected for signal and background processes are shown for the values of nuisance parameters obtained from the combined ML fit and $\mu = \hat{\mu} = $ 1.23, corresponding to the best-fit value from the ML fit. |
png pdf |
Figure 3-b:
Distribution in the discriminating observable used for the signal extraction |
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Figure 3-c:
Distribution in the discriminating observable used for the signal extraction in the $3 {\ell}$ category, compared to the SM expectation for the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal and for background processes. A $ {\mathrm {D}_{\text {MVA}}}$ variable, combining the outputs of two BDTs trained to discriminate the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal from the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\mathrm {V}}$ and $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\text {+jets}}$ backgrounds respectively, is used. The distributions expected for signal and background processes are shown for the values of nuisance parameters obtained from the combined ML fit and $\mu = \hat{\mu} = $ 1.23, corresponding to the best-fit value from the ML fit. |
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Figure 3-d:
Distribution in the discriminating observable used for the signal extraction in the $3 {\ell}+ 1 {{\tau}_{\mathrm {h}}} $ category, compared to the SM expectation for the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal and for background processes. A $ {\mathrm {D}_{\text {MVA}}}$ variable, combining the outputs of two BDTs trained to discriminate the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal from the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\mathrm {V}}$ and $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\text {+jets}}$ backgrounds respectively, is used. The distributions expected for signal and background processes are shown for the values of nuisance parameters obtained from the combined ML fit and $\mu = \hat{\mu} = $ 1.23, corresponding to the best-fit value from the ML fit. |
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Figure 4:
Number of events observed and expected in the $4 {\ell}$ category. The distributions expected for signal and background processes are shown for the values of nuisance parameters obtained from the combined ML fit and $\mu = \hat{\mu} = $ 1.23, corresponding to the best-fit value from the ML fit. |
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Figure 5:
Distribution of the decimal logarithm of the ratio between the expected signal and expected background in each bin of the distributions used for the signal extraction. The distributions expected for signal and background processes are shown for the values of nuisance parameters obtained from the combined ML fit and $\mu = \hat{\mu}=$ 1.23, corresponding to the best-fit value from the ML fit. |
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Figure 6:
Signal rates $\mu $, in units of the SM $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ production rate, measured in each of the categories individually and for the combination of all six categories. |
| Tables | |
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Table 1:
Event selections applied in the $2 {\ell} {\text {ss}}$, $2 {\ell} {\text {ss}}+1 {{\tau}_{\mathrm {h}}} $, $3 {\ell}$, and $3 {\ell}+1 {{\tau}_{\mathrm {h}}} $ categories. |
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Table 2:
Event selections applied in the $1 {\ell}+2 {{\tau}_{\mathrm {h}}} $ and $4 {\ell}$ categories. |
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Table 3:
Observables used as input to the BDTs that separate the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal from the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\mathrm {V}}$ and $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}}\text {+jets}}$ backgrounds in the $1 {\ell}+2 {{\tau}_{\mathrm {h}}} $, $2 {\ell} {\text {ss}}$, $3 {\ell}$, and $3 {\ell}+1 {{\tau}_{\mathrm {h}}} $ categories. |
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Table 4:
Summary of the main sources of systematic uncertainty and their impact on the combined measured $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal rate $\mu $. $\Delta \mu /\mu $ corresponds to the relative shift in signal strength obtained from varying the systematic source within its associated uncertainty. |
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Table 5:
Numbers of events selected in the different categories compared to the SM expectations for the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal and background processes. The event yields expected for the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal and for the backgrounds are shown for the values of nuisance parameters obtained from the ML fit and $\mu = 1$. Quoted uncertainties represent the combination of statistical and systematic components. |
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Table 6:
The 95% {\text {CL}} upper limits on the $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal rate, in units of the SM $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ production rate, obtained in each of the categories individually and for the combination of all six event categories. The observed limit is compared to the limits expected for the background-only hypothesis ($\mu =$ 0) and for the case that a $ {{{\mathrm {t}}} {{\overline {\mathrm {t}}}} {{\mathrm {H}}}}$ signal of SM production rate is present in the data ($\mu =1$). The $ \pm $1 standard deviation uncertainty intervals on the expected limits are also given in the table. |
| Summary |
| A search has been presented for the associated production of a Higgs boson with a top quark pair in final states with electrons, muons, and hadronically decaying $\tau$ leptons (${\tau_\mathrm{h}}$). The analyzed data set corresponds to an integrated luminosity of 35.9 fb$^{-1}$ of ${\mathrm{p}}{\mathrm{p}}$ collision data recorded by the CMS experiment at $\sqrt{s} = $ 13 TeV. The analysis is performed in six mutually exclusive event categories, based on different lepton and ${\tau_\mathrm{h}}$ multiplicity requirements. The sensitivity of the analysis is enhanced by using multivariate analysis techniques based on boosted decision trees and on the matrix element method. The results of the analysis are in agreement with the standard model (SM) expectation. The measured signal rate amounts to 1.23$^{+0.45}_{-0.43}$ times the SM $\mathrm{ t \bar{t} H }$ production rate, with an observed (expected) significance of 3.2$\sigma$ (2.8$\sigma$), which represents evidence for $\mathrm{ t \bar{t} H }$ production in those final states. An upper limit on the signal rate of 2.1 times the SM $\mathrm{ t \bar{t} H }$ production rate is set at 95% confidence level, for an expected limit of 1.7 times the SM production rate in the presence of a $\mathrm{ t \bar{t} H }$ signal. |
| Additional Figures | |
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Additional Figure 1:
Relative contribution of the $\mathrm {t \bar{t} H }$, H $\rightarrow $ WW, ZZ and $\tau \tau$ decay modes in the different analysis categories. |
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Additional Figure 2:
Signal rates $\mu $, in units of the SM $\mathrm {t \bar{t} H } $ production rate, measured either assuming the same signal strength for all decay modes (combined) or independently for the Higgs boson decays into two electroweak bosons ($\mathrm {t \bar{t} H } + \mathrm{ H \to VV} $) and into two $\tau $ leptons ($\mathrm {t \bar{t} H } + \mathrm{ H\to \tau \bar{\tau}} $). |
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Additional Figure 3:
The 95% CL upper limits on the $\mathrm {t \bar{t} H }$ signal rate, obtained in each of the categories individually and for the combination of all six event categories. The expected limits are computed for the background-only ($\mu =$ 0) and signal ($\mu =$ 1) plus background hypotheses. |
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